Query-Count-Independent Differentially Private Synopses for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) can expose protected records through repeated access to its knowledge base. Query-time differential privacy (DP) requires cumulative accounting, which can constrain utility under a fixed total budget. Reusable DP synthetic corpora avoid per-query expenditure but face the challenge of preserving evidence for downstream retrieval. We propose SynopsisRAG, an offline-release framework that uses protected evidence to guide the private selection of intact public passages. A bounded fidelity score combines assessed answer completeness with estimated private-evidence support, guiding bidirectional reweighting and privacy-calibrated sampling of one representative per category. Online retrieval and generation then reuse the released synopsis without accessing the private corpus or incurring additional privacy loss for that version. We establish pure chunk-level DP with a direct document-level extension, derive conditional answer-support bounds for the released synopsis, and quantify how privacy calibration limits release concentration. On healthcare and finance workloads of up to 10,000 queries, SynopsisRAG achieves higher long-horizon match accuracy than the evaluated query-time and synthetic-corpus DP baselines. Extraction tests additionally show reductions in measured token leakage.